System and process for automatically explaining probabilistic predictions

• US 20050091245A1
• Filed: 11/17/2004
• Published: 04/28/2005
• Est. Priority Date: 05/24/2001
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Rounthwaite, Robert, Chickering, David Maxwell, Heckerman, David E.
• Granted Patent: US 7,389,288 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: Y10S 707/99932 Access augmentation or optimizing
• CPC Class Codes: Y10S 707/99933 Query processing, i.e. searching
• CPC Class Codes: Y10S 707/99934 Query formulation, input preparation, or translation
• CPC Class Codes: Y10S 707/99943 Generating database or data structure, e.g. via user interface

Abstract

The system and method of the present invention automatically assigns “scores” to the predictor/variable value pairs of a conventional probabilistic model to measure the relative impact or influence of particular elements of a set of topics, items, products, etc. in making specific predictions using the probabilistic model. In particular, these scores measure the relative impact, either positive or negative, that the value of each individual predictor variable has on the posterior distribution of the target topic, item, product, etc., for which a probability is being determined. These scores are useful for understanding why each prediction is made, and how much impact each predictor has on the prediction. Consequently, such scores are useful for explaining why a particular prediction or recommendation was made.

Claims

1. A method in a computer system for determining an influence of at least one possible user choice on at least one prediction of likely user choices, comprising using a computer to perform the following steps:

a step for predicting at least one likely user choice based on a probabilistic model and a set of preferences for the user;

a step for determining the influence of at least one of the possible user choices on the prediction of at least one likely user choice by calculating probabilistic explanation scores for each of at least one possible user choice, said explanation scores representing a probabilistic impact of predictor/variable value pairs on a posterior distribution corresponding to the prediction of each likely user choice;

a step for sorting the explanation scores; and

a step for providing one or more of the sorted explanation scores via a user interface.

2. The method of claim 1 further comprising a step for implicitly determining the set of user preferences.

3. The method of claim 1 further comprising a step for implicitly determining the set of user preferences.

5. The method of claim 1 wherein the step for sorting the explanation scores includes a step for sorting each explanation score in order from highest to lowest.

6. The method of claim 5 further comprising a step for providing a predetermined number of highest value explanation scores from the sorted explanation scores via the user interface.

7. The method of claim 1 further comprising a step for automatically providing one or more highest value explanation scores from the sorted explanation scores as part of a natural language explanation for explaining each prediction of likely user choices.

8. The method of claim 5 further comprising a step for providing a predetermined number of lowest value explanation scores from the sorted explanation scores via the user interface.

9. The method of claim 5 further comprising a step for providing one or more of the sorted explanation scores via the user interface as part of a natural language explanation for explaining each prediction of likely user choices.

4. (canceled).

10. In a computer system having a graphical user interface including a display and a user interface selection device, a method for automatically assigning scores to members of a set of at least one predictor/variable value pair representing likely user choices, comprising the steps of:

obtaining a set of user preferences for a particular user;

computing predictor/variable value pairs for each possible user choice using the set of user preferences in combination with a probabilistic model;

computing at least one prediction of likely user choices based upon the predictor/variable value pairs;

calculating explanation scores for at least one of the predictor/variable value pairs for each prediction;

determining the influence of at least one member of the set of predictor/variable value pairs for each prediction based upon the scores calculated for the predictor/variable value pairs; and

wherein at least one explanation score is provided via a user interface as part of natural language explanation for explaining why each prediction of likely user choices was made.

11. The computer system of claim 10 wherein the set of user preferences is obtained by examining a set of responses to a set of questions provided to the particular user.

12. The computer system of claim 10 wherein the set of user preferences is obtained by automatically examining at least one pattern of behavior of the particular user.

13. The computer system of claim 10 wherein the set of user preferences is obtained automatically by using a combination of implicit and explicit methods for determining the preferences of the particular user.

14. The computer system of claim 10 wherein the probabilistic model is automatically generated by examining a behavior of a group of at least one user.

15. The computer system of claim 10 wherein the probabilistic model is automatically generated using machine learning techniques.

16. (canceled).

17. A method in a computer system for displaying on a display device automatically identified positive and negative influences on recommendations of likely user choices, the method comprising steps for:

automatically computing predictor/variable value pairs for each possible user choice using a set of user preferences in combination with a probabilistic model;

automatically providing at least one recommendation of likely user choices based upon the predictor/variable value pairs;

automatically calculating an influence of each possible user choice for each recommendation based upon scores calculated for the predictor/variable value pairs, said scores representing a probabilistic impact of each possible user choice on a posterior distribution representing a probability of each recommendation; and

wherein the influence of at least one possible user choice on the at least one recommendation of likely user choices is provided in a human readable format via a user interface.

18. The method of claim 17 further comprising a step for providing the set of user preferences by user input via the user interface.

19. The method of claim 17 further comprising a step for generating the set of user preferences by examining Internet web browsing behavior of a user.

20. The method of claim 17 further comprising a step for automatically learning the probabilistic model using a set behavioral data gathered from a set of at least one user.

21. The method of claim 20 further comprising a step for automatically learning the probabilistic model using machine learning techniques.

22. The method of claim 17 wherein the step for providing the influence of at least one possible user choice in a human readable format further comprises a step for providing the influence as a natural language explanation via the user interface.